Bayesian inference facilitated structured interpretation of
a nonreplicated, experience-based survey of potential nesting
habitat for bald eagles (Haliaeetus leucocephalus) along the
five Great Lakes shorelines. We developed a pattern recognition
(PATREC) model of our aerial search image with six habitat attributes:
(a) tree cover, (b) proximity and (c) type/amount of human disturbance,
(d) potential foraging habitat/shoreline irregularity, and suitable
trees for (e) perching and (f) nesting. Tree cover greater than
10 percent, human disturbance more than 0.8 km away, a ratio
of total to linear shoreline distance greater than 2.0, and suitable
perch and nest trees were prerequisite for good eagle habitat
(having sufficient physical attributes for bald eagle nesting).
The estimated probability of good habitat was high (96 percent)
when all attributes were optimal, and nonexistent (0 percent)
when none of the model attributes were present. Of the 117 active
bald eagle nests along the Great Lakes shorelines in 1992, 82
percent were in habitat classified as good. While our PATREC
model provides a method for consistent interpretation of subjective
surveyor experience, it also facilitates future management of
bald eagle nesting habitat along Great Lakes shorelines by providing
insight into the number, type, and relative importance of key
habitat attributes. This practical application of Bayesian inference
demonstrates the technique's advantages for effectively incorporating
available expertise, detailing model development processes, enabling
exploratory simulations, and facilitating long-term ecosystem
monitoring.